In Magnetic Resonance Imaging (MRI), an object is subjected to a high-strength constant ambient field and an RF pulse sequence is applied. The scanner measures the resulting magnetization in a transverse plane. The transient trajectory depends on, among other things, the tissue parameters associated with the object (e.g., longitudinal relaxation time, transverse relaxation time, and proton density). Traditionally, a pulse sequence is designed to generate contrast by emphasizing a single parameter. Thus, to get a qualitative analysis of the object for all the parameters, there must be multiple, successive acquisitions. The pulse sequence must be carefully designed and acquisitions separated to ensure independence of successive measurements. As a result, the total scan time must increase substantially to ensure that sufficient measurements have been acquired.
Magnetic Resonance Fingerprinting (MRF) is a technique which addresses these issues by replacing the sequence design with one that continuously generates contrast with different parameters emphasis such that all the quantitative parameters may be acquired. In MRF, instead of exciting the system such that it's always in the same state, the state varies based on different excitations (flip angles, repetition times). Depending on all the parameters involved in a particular acquisition, the variation of the magnetization trajectory will be different. Following all the acquisitions, the various magnetization trajectories can be matched to recover the original parameters.
One conventional MRF technique is referred to as direct MRF. This technique uses a spiral readout acquisition and reconstructs one image per readout. The acquisition is highly undersampled and results in heavy undersampling artifacts in the images. Briefly, a Fourier transformation is applied to the acquired data to produce a set of images. For each voxel in the images, the technique determines a static set of parameters that fit the magnetization over time. Fitting is performed by comparing time curves with a dictionary of simulations. The technique itself is computationally and time intensive because the dictionary must be recomputed if the pulse sequence changes. Also, this technique is non-iterative and the resulting parameter maps can still show undersampling artifacts.
Another conventional MRF technique is iterative MRF. For a given sequence, the map of all possible parameters by the simulation function is a low-dimensional manifold. To reconstruct the image, a magnetization map in the manifold is determined that is compatible with the measurements, as well as the parameters that generated it. A projected gradient descent algorithm is applied where the gradient step decreases the error form measurements and the projection step projects magnetization back onto the manifold. This technique reduces undersampling artifacts compared to the direct MRF reconstruction technique described above. However, the projection is still performed by matching with a dictionary generated from sequence-dependent simulations.
Aside from the drawbacks set forth above, MRF techniques which are dependent on a dictionary have disadvantages which limit their applicability across real-world scenarios. For example, when a dictionary is constructed, there is an assumption that the underlying parameters remain constant or substantially similar throughout the acquisition, ignoring any motion that may occur in the region of interest. As a result, a dictionary-based MRF system cannot handle situations where a subject moves during imaging. Similarly, dictionary-based MRF systems are ill-suited to clinical applications such as cardiac imaging where motion is inherent and must be accounted for in order to provide accurate results. Accordingly, it is desired to provide techniques for acquiring MR parameter maps which provide the benefits of MRF without the problems associated with dictionary-based implementations.